Simulation of Extreme Fire Event Scenarios Using Fully Physical Models and Visualisation Systems

[thumbnail of 978-3-031-56114-6_5.pdf]
Preview
978-3-031-56114-6_5.pdf - Published Version (861kB) | Preview
Available under license: Creative Commons Attribution

Moinuddin, Khalid ORCID logoORCID: https://orcid.org/0000-0002-1831-6754, Tirado Cortes, Carlos, Hassan, Ahmad A ORCID logoORCID: https://orcid.org/0009-0005-1801-5889, Accary, Gilbert and Wu, Frank (2024) Simulation of Extreme Fire Event Scenarios Using Fully Physical Models and Visualisation Systems. In: Arts, Research, Innovation and Society. Springer Nature Switzerland, pp. 49-63.

Abstract

Although extreme wildland fires used to be rare events, their frequency has been increasing, and they are now causing enormous destruction. Therefore, understanding such fire events is crucial for global ecological and human communities. Predicting extreme fire events is an imperative yet challenging task. As these destructive events cannot be investigated via experimental field studies, physical modelling can be an alternative. This chapter explores the capability of fully physical fire models to simulate these events and the potential of integrating these simulations with advanced visualisation systems supported by machine learning. By presenting case studies and future directions, we emphasise the potential and necessity of this integration for improved fire management and policy making.

Dimensions Badge

Altmetric Badge

Item type Book Section
URI https://vuir.vu.edu.au/id/eprint/49592
DOI 10.1007/978-3-031-56114-6_5
Official URL https://doi.org/10.1007/978-3-031-56114-6_5
ISBN 9783031561139
Subjects Current > FOR (2020) Classification > 4005 Civil engineering
Current > Division/Research > Institute for Sustainable Industries and Liveable Cities
Keywords canyon fire, computational fluid dynamics, data visualisation, extreme fire, junction fire, large-eddy simulation, physics-based model, wildfire modelling, wildfire visualisation
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login